A Simple Clinically Based Predictive Rule for Heart Failure In-Hospital Mortality

Abstract 

Background

Scarce data are available to predict in-hospital mortality for decompensated heart failure (HF) in South American populations.

Methods and Results

We evaluated 779 consecutive HF admissions defined by the Boston criteria in a tertiary care hospital. Stepwise logistic regression was used to determine independent correlates of in-hospital mortality, derived from 83 potential predictors collected on hospital admission. A clinical score rule (HF Revised Score) was created using the regression coefficient estimates derived from multivariate modeling. During hospital stay, 77 (10%) deaths occurred and 6 clinical characteristics were independently associated with in-hospital mortality: presence of cancer (P < .001), systolic blood pressure ≤124 mm Hg (P < .001), serum creatinine >1.4 mg/dL (P = .02), blood urea nitrogen >37 mg/dL (P = .03), serum sodium <136 mEq/L (P = .03), and age >70 years old (P = .03). Both the Acute Decompensated Heart Failure National Registry stratification algorithm and the proposed HF Revised Score performed adequately to predict in-hospital mortality (“c” statistics = 0.71 and 0.76, respectively). The newly proposed score, however, discriminated a very low-risk group (101 [13%]) in whom all patients were discharged home, representing patients admitted with none of the 6 predictors of risk.

Conclusion

HF risk stratification can be accurately accomplished during the first day of admission with simple and easily obtained clinical variables.

Key Words: Heart failure, Mortality, Prognosis, Predictive rule